Segmenting audio signals into auditory events

ABSTRACT

In one aspect, the invention divides an audio signal into auditory events, each of which tends to be perceived as separate and distinct, by calculating the spectral content of successive time blocks of the audio signal, calculating the difference in spectral content between successive time blocks of the audio signal, and identifying an auditory event boundary as the boundary between successive time blocks when the difference in the spectral content between such successive time blocks exceeds a threshold. In another aspect, the invention generates a reduced-information representation of an audio signal by dividing an audio signal into auditory events, each of which tends to be perceived as separate and distinct, and formatting and storing information relating to the auditory events. Optionally, the invention may also assign a characteristic to one or more of the auditory events. Auditory events may be determined according to the first aspect of the invention or by another method.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/478,538 filed on Nov. 20, 2003, which is a National Stage of PCTapplication PCT/US02/05999 filed on Feb. 26, 2002. PCT applicationPCT/US02/05999 also claims the benefit of PCT/US02/04317 filed on Feb.12, 2002, which is, in turn, a continuation-in-part of U.S. patentapplication Ser. No. 10/045,644 filed on Jan. 11, 2002, which is, inturn, a continuation-in-part of U.S. patent application Ser. No.09/922,394 filed on Aug. 2, 2001, and which is, in turn, a continuationof U.S. patent application Ser. No. 09/834,739, filed Apr. 13, 2001. PCTapplication PCT/US02/05999 also claims the benefit of U.S. ProvisionalApplication Ser. No. 60/351,498 filed on Jan. 23, 2002. PCT ApplicationPCT/US02/05999 is a continuation-in-part of U.S. patent application Ser.No. 10/045,644 filed on Jan. 11, 2002 which is, in turn, acontinuation-in-part of U.S. patent application Ser. No. 09/922,394filed on Aug. 2, 2001, and which is, in turn, a continuation of U.S.patent application Ser. No. 09/834,739, filed Apr. 13, 2001. PCTapplication PCT/US02/05999 also claims the benefit of U.S. ProvisionalApplication Ser. No. 60/293,825 filed on May 25, 2001.

TECHNICAL FIELD

The present invention pertains to the field of psychoacoustic processingof audio signals. In particular, the invention relates to aspects ofdividing or segmenting audio signals into “auditory events,” each ofwhich tends to be perceived as separate and distinct, and to aspects ofgenerating reduced-information representations of audio signals based onauditory events and, optionally, also based on the characteristics orfeatures of audio signals within such auditory events. Auditory eventsmay be useful as defining the MPEG-7 “Audio Segments” as proposed by the“ISO/IEC JTC 1/SC 29/WG 11.”

BACKGROUND ART

The division of sounds into units or segments perceived as separate anddistinct is sometimes referred to as “auditory event analysis” or“auditory scene analysis” (“ASA”). An extensive discussion of auditoryscene analysis is set forth by Albert S. Bregman in his book AuditoryScene Analysis—The Perceptual Organization of Sound, MassachusettsInstitute of Technology, 1991, Fourth printing, 2001, Second MIT Presspaperback edition.) In addition, U.S. Pat. No. 6,002,776 to Bhadkamkar,et al, Dec. 14, 1999 cites publications dating back to 1976 as “priorart work related to sound separation by auditory scene analysis.”However, the Bhadkamkar, et al patent discourages the practical use ofauditory scene analysis, concluding that “[t]echniques involvingauditory scene analysis, although interesting from a scientific point ofview as models of human auditory processing, are currently far toocomputationally demanding and specialized to be considered practicaltechniques for sound separation until fundamental progress is made.”

There are many different methods for extracting characteristics orfeatures from audio. Provided the features or characteristics aresuitably defined, their extraction can be performed using automatedprocesses. For example “ISO/IEC JTC 1/SC 29/WG 11” (MPEG) is currentlystandardizing a variety of audio descriptors as part of the MPEG-7standard. A common shortcoming of such methods is that they ignoreauditory scene analysis. Such methods seek to measure, periodically,certain “classical” signal processing parameters such as pitch,amplitude, power, harmonic structure and spectral flatness. Suchparameters, while providing useful information, do not analyze andcharacterize audio signals into elements perceived as separate anddistinct according to human cognition. However, MPEG-7 descriptors maybe useful in characterizing an Auditory Event identified in accordancewith aspects of the present invention.

DISCLOSURE OF THE INVENTION

In accordance with aspects of the present invention, a computationallyefficient process for dividing audio into temporal segments or “auditoryevents” that tend to be perceived as separate and distinct is provided.The locations of the boundaries of these auditory events (where theybegin and end with respect to time) provide valuable information thatcan be used to describe an audio signal. The locations of auditory eventboundaries can be assembled to generate a reduced-informationrepresentation, “signature, or “fingerprint” of an audio signal that canbe stored for use, for example, in comparative analysis with othersimilarly generated signatures (as, for example, in a database of knownworks).

Bregman notes that “[w]e hear discrete units when the sound changesabruptly in timbre, pitch, loudness, or (to a lesser extent) location inspace.” (Auditory Scene Analysis—The Perceptual Organization of Sound,supra at page 469). Bregman also discusses the perception of multiplesimultaneous sound streams when, for example, they are separated infrequency.

In order to detect changes in timbre and pitch and certain changes inamplitude, the audio event detection process according to an aspect ofthe present invention detects changes in spectral composition withrespect to time. When applied to a multichannel sound arrangement inwhich the channels represent directions in space, the process accordingto an aspect of the present invention also detects auditory events thatresult from changes in spatial location with respect to time.Optionally, according to a further aspect of the present invention, theprocess may also detect changes in amplitude with respect to time thatwould not be detected by detecting changes in spectral composition withrespect to time.

In its least computationally demanding implementation, the processdivides audio into time segments by analyzing the entire frequency band(full bandwidth audio) or substantially the entire frequency band (inpractical implementations, band limiting filtering at the ends of thespectrum is often employed) and giving the greatest weight to theloudest audio signal components. This approach takes advantage of apsychoacoustic phenomenon in which at smaller time scales (20milliseconds (ms) and less) the ear may tend to focus on a singleauditory event at a given time. This implies that while multiple eventsmay be occurring at the same time, one component tends to beperceptually most prominent and may be processed individually as thoughit were the only event taking place. Taking advantage of this effectalso allows the auditory event detection to scale with the complexity ofthe audio being processed. For example, if the input audio signal beingprocessed is a solo instrument, the audio events that are identifiedwill likely be the individual notes being played. Similarly for an inputvoice signal, the individual components of speech, the vowels andconsonants for example, will likely be identified as individual audioelements. As the complexity of the audio increases, such as music with adrumbeat or multiple instruments and voice, the auditory event detectionidentifies the “most prominent” (i.e., the loudest) audio element at anygiven moment. Alternatively, the most prominent audio element may bedetermined by taking hearing threshold and frequency response intoconsideration.

While the locations of the auditory event boundaries computed fromfull-bandwidth audio provide useful information related to the contentof an audio signal, it might be desired to provide additionalinformation further describing the content of an auditory event for usein audio signal analysis. For example, an audio signal could be analyzedacross two or more frequency subbands and the location of frequencysubband auditory events determined and used to convey more detailedinformation about the nature of the content of an auditory event. Suchdetailed information could provide additional information unavailablefrom wideband analysis.

Thus, optionally, according to further aspects of the present invention,at the expense of greater computational complexity, the process may alsotake into consideration changes in spectral composition with respect totime in discrete frequency subbands (fixed or dynamically determined orboth fixed and dynamically determined subbands) rather than the fullbandwidth. This alternative approach would take into account more thanone audio stream in different frequency subbands rather than assumingthat only a single stream is perceptible at a particular time.

Even a simple and computationally efficient process according to aspectsof the present invention has been found usefully to identify auditoryevents.

An auditory event detecting process according to the present inventionmay be implemented by dividing a time domain audio waveform into timeintervals or blocks and then converting the data in each block to thefrequency domain, using either a filter bank or a time-frequencytransformation, such as the FFT. The amplitude of the spectral contentof each block may be normalized in order to eliminate or reduce theeffect of amplitude changes. Each resulting frequency domainrepresentation provides an indication of the spectral content (amplitudeas a function of frequency) of the audio in the particular block. Thespectral content of successive blocks is compared and changes greaterthan a threshold may be taken to indicate the temporal start or temporalend of an auditory event. FIG. 1 shows an idealized waveform of a singlechannel of orchestral music illustrating auditory events. The spectralchanges that occur as a new note is played trigger the new auditoryevents 2 and 3 at samples 2048 and 2560, respectively.

As mentioned above, in order to minimize the computational complexity,only a single band of frequencies of the time domain audio waveform maybe processed, preferably either the entire frequency band of thespectrum (which may be about 50 Hz to 15 kHz in the case of an averagequality music system) or substantially the entire frequency band (forexample, a band defining filter may exclude the high and low frequencyextremes).

Preferably, the frequency domain data is normalized, as is describedbelow. The degree to which the frequency domain data needs to benormalized gives an indication of amplitude. Hence, if a change in thisdegree exceeds a predetermined threshold, that too may be taken toindicate an event boundary. Event start and end points resulting fromspectral changes and from amplitude changes may be ORed together so thatevent boundaries resulting from either type of change are identified.

In the case of multiple audio channels, each representing a direction inspace, each channel may be treated independently and the resulting eventboundaries for all channels may then be ORed together. Thus, forexample, an auditory event that abruptly switches directions will likelyresult in an “end of event” boundary in one channel and a “start ofevent” boundary in another channel. When ORed together, two events willbe identified. Thus, the auditory event detection process of the presentinvention is capable of detecting auditory events based on spectral(timbre and pitch), amplitude and directional changes.

As mentioned above, as a further option, but at the expense of greatercomputational complexity, instead of processing the spectral content ofthe time domain waveform in a single band of frequencies, the spectrumof the time domain waveform prior to frequency domain conversion may bedivided into two or more frequency bands. Each of the frequency bandsmay then be converted to the frequency domain and processed as though itwere an independent channel in the manner described above. The resultingevent boundaries may then be ORed together to define the eventboundaries for that channel. The multiple frequency bands may be fixed,adaptive, or a combination of fixed and adaptive. Tracking filtertechniques employed in audio noise reduction and other arts, forexample, may be employed to define adaptive frequency bands (e.g.,dominant simultaneous sine waves at 800 Hz and 2 kHz could result in twoadaptively-determined bands centered on those two frequencies). Althoughfiltering the data before conversion to the frequency domain isworkable, more optimally the full bandwidth audio is converted to thefrequency domain and then only those frequency subband components ofinterest are processed. In the case of converting the full bandwidthaudio using the FFT, only sub-bins corresponding to frequency subbandsof interest would be processed together.

Alternatively, in the case of multiple subbands or multiple channels,instead of ORing together auditory event boundaries, which results insome loss of information, the event boundary information may bepreserved.

As shown in FIG. 2, the frequency domain magnitude of a digital audiosignal contains useful frequency information out to a frequency of Fs/2where Fs is the sampling frequency of the digital audio signal. Bydividing the frequency spectrum of the audio signal into two or moresubbands (not necessarily of the same bandwidth and not necessarily upto a frequency of Fs/2 Hz), the frequency subbands may be analyzed overtime in a manner similar to a full bandwidth auditory event detectionmethod.

The subband auditory event information provides additional informationabout an audio signal that more accurately describes the signal anddifferentiates it from other audio signals. This enhanceddifferentiating capability may be useful if the audio signatureinformation is to be used to identify matching audio signals from alarge number of audio signatures. For example, as shown in FIG. 2, afrequency subband auditory event analysis (with a auditory eventboundary resolution of 512 samples) has found multiple subband auditoryevents starting, variously, at samples 1024 and 1536 and ending,variously, at samples 2560, 3072 and 3584. It is unlikely that thislevel of signal detail would be available from a single, widebandauditory scene analysis.

The subband auditory event information may be used to derive an auditoryevent signature for each subband. While this would increase the size ofthe audio signal's signature and possibly increase the computation timerequired to compare multiple signatures it could also greatly reduce theprobability of falsely classifying two signatures as being the same. Atradeoff between signature size, computational complexity and signalaccuracy could be done depending upon the application. Alternatively,rather than providing a signature for each subband, the auditory eventsmay be ORed together to provide a single set of “combined” auditoryevent boundaries (at samples 1024, 1536, 2560, 3072 and 3584. Althoughthis would result in some loss of information, it provides a single setof event boundaries, representing combined auditory events, thatprovides more information than the information of a single subband or awideband analysis.

While the frequency subband auditory event information on its ownprovides useful signal information, the relationship between thelocations of subband auditory events may be analyzed and used to providemore insight into the nature of an audio signal. For example, thelocation and strength of the subband auditory events may be used as anindication of timbre (frequency content) of the audio signal. Auditoryevents that appear in subbands that are harmonically related to oneanother would also provide useful insight regarding the harmonic natureof the audio. The presence of auditory events in a single subband mayalso provide information as to the tone-like nature of an audio signal.Analyzing the relationship of frequency subband auditory events acrossmultiple channels can also provide spatial content information.

In the case of analyzing multiple audio channels, each channel isanalyzed independently and the auditory event boundary information ofeach may either be retained separately or be combined to providecombined auditory event information. This is somewhat analogous to thecase of multiple subbands. Combined auditory events may be betterunderstood by reference to FIG. 3 that shows the auditory scene analysisresults for a two channel audio signal. FIG. 3 shows time concurrentsegments of audio data in two channels. ASA processing of the audio in afirst channel, the top waveform of FIG. 3, identifies auditory eventboundaries at samples that are multiples of the 512 samplespectral-profile block size, 1024 and 1536 samples in this example. Thelower waveform of FIG. 3 is a second channel and ASA processing resultsin event boundaries at samples that are also multiples of thespectral-profile block size, at samples 1024, 2048 and 3072 in thisexample. A combined auditory event analysis for both channels results incombined auditory event segments with boundaries at samples 1024, 1536,2048 and 3072 (the auditory event boundaries of the channels are “ORed”together). It will be appreciated that in practice the accuracy ofauditory event boundaries depends on the size of the spectral-profileblock size (N is 512 samples in this example) because event boundariescan occur only at block boundaries. Nevertheless, a block size of 512samples has been found to determine auditory event boundaries withsufficient accuracy as to provide satisfactory results.

FIG. 3A shows three auditory events. These events include the (1) quietportion of audio before the transient, (2) the transient event, and (3)the echo/sustain portion of the audio transient. A speech signal isrepresented in FIG. 3B having a predominantly high-frequency sibilanceevent, and events as the sibilance evolves or “morphs” into the vowel,the first half of the vowel, and the second half of the vowel.

FIG. 3 also shows the combined event boundaries when the auditory eventdata is shared across the time concurrent data blocks of two channels.Such event segmentation provides five combined auditory event regions(the event boundaries are ORed together).

FIG. 4 shows an example of a four channel input signal. Channels 1 and 4each contain three auditory events and channels 2 and 3 each contain twoauditory events. The combined auditory event boundaries for theconcurrent data blocks across all four channels are located at samplenumbers 512, 1024, 1536, 2560 and 3072 as indicated at the bottom of theFIG. 4.

In principle, the processed audio may be digital or analog and need notbe divided into blocks. However, in practical applications, the inputsignals likely are one or more channels of digital audio represented bysamples in which consecutive samples in each channel are divided intoblocks of, for example 4096 samples (as in the examples of FIGS. 1, 3and 4, above). In practical embodiments set forth herein, auditoryevents are determined by examining blocks of audio sample datapreferably representing approximately 20 ms of audio or less, which isbelieved to be the shortest auditory event recognizable by the humanear. Thus, in practice, auditory events are likely to be determined byexamining blocks of, for example, 512 samples, which corresponds toabout 11.6 ms of input audio at a sampling rate of 44.1 kHz, withinlarger blocks of audio sample data. However, throughout this documentreference is made to “blocks” rather than “subblocks” when referring tothe examination of segments of audio data for the purpose of detectingauditory event boundaries. Because the audio sample data is examined inblocks, in practice, the auditory event temporal start and stop pointboundaries necessarily will each coincide with block boundaries. Thereis a trade off between real-time processing requirements (as largerblocks require less processing overhead) and resolution of eventlocation (smaller blocks provide more detailed information on thelocation of auditory events).

Other aspects of the invention will be appreciated and understood as thedetailed description of the invention is read and understood.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an idealized waveform of a single channel of orchestral musicillustrating auditory.

FIG. 2 is an idealized conceptual schematic diagram illustrating theconcept of dividing full bandwidth audio into frequency subbands inorder to identify subband auditory events. The horizontal scale issamples and the vertical scale is frequency.

FIG. 3 is a series of idealized waveforms in two audio channels, showingaudio events in each channel and combined audio events across the twochannels.

FIG. 4 is a series of idealized waveforms in four audio channels showingaudio events in each channel and combined audio events across the fourchannels.

FIG. 5 is a flow chart showing the extraction of audio event locationsand the optional extraction of dominant subbands from an audio signal inaccordance with the present invention.

FIG. 6 is a conceptual schematic representation depicting spectralanalysis in accordance with the present invention.

FIGS. 7-9 are flow charts showing more generally three alternativearrangements equivalent to the flow chart of FIG. 5.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In accordance with an embodiment of one aspect of the present invention,auditory scene analysis is composed of three general processing steps asshown in a portion of FIG. 5. The first step 5-1 (“Perform SpectralAnalysis”) takes a time-domain audio signal, divides it into blocks andcalculates a spectral profile or spectral content for each of theblocks. Spectral analysis transforms the audio signal into theshort-term frequency domain. This can be performed using any filterbank,either based on transforms or banks of bandpass filters, and in eitherlinear or warped frequency space (such as the Bark scale or criticalband, which better approximate the characteristics of the human ear).With any filterbank there exists a tradeoff between time and frequency.Greater time resolution, and hence shorter time intervals, leads tolower frequency resolution. Greater frequency resolution, and hencenarrower subbands, leads to longer time intervals.

The first step, illustrated conceptually in FIG. 6 calculates thespectral content of successive time segments of the audio signal. In apractical embodiment, the ASA block size is 512 samples of the inputaudio signal. In the second step 5-2, the differences in spectralcontent from block to block are determined (“Perform spectral profiledifference measurements”). Thus, the second step calculates thedifference in spectral content between successive time segments of theaudio signal. As discussed above, a powerful indicator of the beginningor end of a perceived auditory event is believed to be a change inspectral content. In the third step 5-3 (“Identify location of auditoryevent boundaries”), when the spectral difference between onespectral-profile block and the next is greater than a threshold, theblock boundary is taken to be an auditory event boundary. The audiosegment between consecutive boundaries constitutes an auditory event.Thus, the third step sets an auditory event boundary between successivetime segments when the difference in the spectral profile contentbetween such successive time segments exceeds a threshold, thus definingauditory events. In this embodiment, auditory event boundaries defineauditory events having a length that is an integral multiple of spectralprofile blocks with a minimum length of one spectral profile block (512samples in this example). In principle, event boundaries need not be solimited. As an alternative to the practical embodiments discussedherein, the input block size may vary, for example, so as to beessentially the size of an auditory event.

The locations of event boundaries may be stored as a reduced-informationcharacterization or “signature” and formatted as desired, as shown instep 5-4. An optional process step 5-5 (“Identify dominant subband”)uses the spectral analysis of step 5-1 to identify a dominant frequencysubband that may also be stored as part of the signature. The dominantsubband information may be combined with the auditory event boundaryinformation in order to define a feature of each auditory event.

Either overlapping or non-overlapping segments of the audio may bewindowed and used to compute spectral profiles of the input audio.Overlap results in finer resolution as to the location of auditoryevents and, also, makes it less likely to miss an event, such as atransient. However, overlap also increases computational complexity.Thus, overlap may be omitted. FIG. 6 shows a conceptual representationof non-overlapping 512 sample blocks being windowed and transformed intothe frequency domain by the Discrete Fourier Transform (DFT). Each blockmay be windowed and transformed into the frequency domain, such as byusing the DFT, preferably implemented as a Fast Fourier Transform (FFT)for speed.

The following variables may be used to compute the spectral profile ofthe input block:

-   -   N=number of samples in the input signal    -   M=number of windowed samples in a block used to compute spectral        profile    -   P=number of samples of spectral computation overlap    -   Q=number of spectral windows/regions computed

In general, any integer numbers may be used for the variables above.However, the implementation will be more efficient if M is set equal toa power of 2 so that standard FFTs may be used for the spectral profilecalculations. In addition, if N, M, and P are chosen such that Q is aninteger number, this will avoid under-running or over-running audio atthe end of the N samples. In a practical embodiment of the auditoryscene analysis process, the parameters listed may be set to:

-   -   M=512 samples (or 11.6 ms at 44.1 kHz)    -   P=0 samples (no overlap)

The above-listed values were determined experimentally and were foundgenerally to identify with sufficient accuracy the location and durationof auditory events. However, setting the value of P to 256 samples (50%overlap) rather than zero samples (no overlap) has been found to beuseful in identifying some hard-to-find events. While many differenttypes of windows may be used to minimize spectral artifacts due towindowing, the window used in the spectral profile calculations is anM-point Hanning, Kaiser-Bessel or other suitable, preferablynon-rectangular, window. The above-indicated values and a Hanning windowtype were selected after extensive experimental analysis as they haveshown to provide excellent results across a wide range of audiomaterial. Non-rectangular windowing is preferred for the processing ofaudio signals with predominantly low frequency content. Rectangularwindowing produces spectral artifacts that may cause incorrect detectionof events. Unlike certain encoder/decoder (codec) applications where anoverall overlap/add process must provide a constant level, such aconstraint does not apply here and the window may be chosen forcharacteristics such as its time/frequency resolution and stop-bandrejection.

In step 5-1 (FIG. 5), the spectrum of each M-sample block may becomputed by windowing the data by an M-point Hanning, Kaiser-Bessel orother suitable window, converting to the frequency domain using anM-point Fast Fourier Transform, and calculating the magnitude of thecomplex FFT coefficients. The resultant data is normalized so that thelargest magnitude is set to unity, and the normalized array of M numbersis converted to the log domain. The array need not be converted to thelog domain, but the conversion simplifies the calculation of thedifference measure in step 5-2. Furthermore, the log domain more closelymatches the nature of the human auditory system. The resulting logdomain values have a range of minus infinity to zero. In a practicalembodiment, a lower limit can be imposed on the range of values; thelimit may be fixed, for example −60 dB, or be frequency-dependent toreflect the lower audibility of quiet sounds at low and very highfrequencies. (Note that it would be possible to reduce the size of thearray to M/2 in that the FFT represents negative as well as positivefrequencies).

Step 5-2 calculates a measure of the difference between the spectra ofadjacent blocks. For each block, each of the M (log) spectralcoefficients from step 5-1 is subtracted from the correspondingcoefficient for the preceding block, and the magnitude of the differencecalculated (the sign is ignored). These M differences are then summed toone number. Hence, for a contiguous time segment of audio, containing Qblocks, the result is an array of Q positive numbers, one for eachblock. The greater the number, the more a block differs in spectrum fromthe preceding block. This difference measure may also be expressed as anaverage difference per spectral coefficient by dividing the differencemeasure by the number of spectral coefficients used in the sum (in thiscase M coefficients).

Step 5-3 identifies the locations of auditory event boundaries byapplying a threshold to the array of difference measures from step 5-2with a threshold value. When a difference measure exceeds a threshold,the change in spectrum is deemed sufficient to signal a new event andthe block number of the change is recorded as an event boundary. For thevalues of M and P given above and for log domain values (in step 5-1)expressed in units of dB, the threshold may be set equal to 2500 if thewhole magnitude FFT (including the mirrored part) is compared or 1250 ifhalf the FFT is compared (as noted above, the FFT represents negative aswell as positive frequencies—for the magnitude of the FFT, one is themirror image of the other). This value was chosen experimentally and itprovides good auditory event boundary detection. This parameter valuemay be changed to reduce (increase the threshold) or increase (decreasethe threshold) the detection of events.

For an audio signal consisting of Q blocks (of size M samples), theoutput of step 5-3 of FIG. 5 may be stored and formatted in step 5-4 asan array B(q) of information representing the location of auditory eventboundaries where q=0, 1, . . . , Q−1. For a block size of M=512 samples,overlap of P=0 samples and a signal-sampling rate of 44.1 kHz, theauditory scene analysis function 2 outputs approximately 86 values asecond. The array B(q) may stored as a signature, such that, in itsbasic form, without the optional dominant subband frequency informationof step 5-5, the audio signal's signature is an array B(q) representinga string of auditory event boundaries.

Identify Dominant Subband Optional

For each block, an optional additional step in the processing of FIG. 5is to extract information from the audio signal denoting the dominantfrequency “subband” of the block (conversion of the data in each blockto the frequency domain results in information divided into frequencysubbands). This block-based information may be converted toauditory-event based information, so that the dominant frequency subbandis identified for every auditory event. Such information for everyauditory event provides information regarding the auditory event itselfand may be useful in providing a more detailed and uniquereduced-information representation of the audio signal. The employmentof dominant subband information is more appropriate in the case ofdetermining auditory events of full bandwidth audio rather than cases inwhich the audio is broken into subbands and auditory events aredetermined for each subband.

The dominant (largest amplitude) subband may be chosen from a pluralityof subbands, three or four, for example, that are within the range orband of frequencies where the human ear is most sensitive.Alternatively, other criteria may be used to select the subbands. Thespectrum may be divided, for example, into three subbands. Usefulfrequency ranges for the subbands are (these particular frequencies arenot critical):

Subband 1  300 Hz to 550 Hz Subband 2  550 Hz to 2000 Hz Subband 3 2000Hz to 10,000 Hz

To determine the dominant subband, the square of the magnitude spectrum(or the power magnitude spectrum) is summed for each subband. Thisresulting sum for each subband is calculated and the largest is chosen.The subbands may also be weighted prior to selecting the largest. Theweighting may take the form of dividing the sum for each subband by thenumber of spectral values in the subband, or alternatively may take theform of an addition or multiplication to emphasize the importance of aband over another. This can be useful where some subbands have moreenergy on average than other subbands but are less perceptuallyimportant.

Considering an audio signal consisting of Q blocks, the output of thedominant subband processing is an array DS(q) of informationrepresenting the dominant subband in each block (q=0, 1, . . . , Q−1).Preferably, the array DS(q) is formatted and stored in the signaturealong with the array B(q). Thus, with the optional dominant subbandinformation, the audio signal's signature is two arrays B(q) and DS(q),representing, respectively, a string of auditory event boundaries and adominant frequency subband within each block, from which the dominantfrequency subband for each auditory event may be determined if desired.Thus, in an idealized example, the two arrays could have the followingvalues (for a case in which there are three possible dominant subbands).

1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 (Event Boundaries)

1 2 2 2 2 1 1 1 3 3 3 3 3 3 1 1 (Dominant Subbands)

In most cases, the dominant subband remains the same within eachauditory event, as shown in this example, or has an average value if itis not uniform for all blocks within the event. Thus, a dominant subbandmay be determined for each auditory event and the array DS(q) may bemodified to provide that the same dominant subband is assigned to eachblock within an event.

The process of FIG. 5 may be represented more generally by theequivalent arrangements of FIGS. 7, 8 and 9. In FIG. 7, an audio signalis applied in parallel to an “Identify Auditory Events” function or step7-1 that divides the audio signal into auditory events, each of whichtends to be perceived as separate and distinct and to an optional“Identify Characteristics of Auditory Events” function or step 7-2. Theprocess of FIG. 5 may be employed to divide the audio signal intoauditory events or some other suitable process may be employed. Theauditory event information, which may be an identification of auditoryevent boundaries, determined by function or step 7-1 is stored andformatted, as desired, by a “Store and Format” function or step 7-3. Theoptional “Identify Characteristics” function or step 7-3 also receivesthe auditory event information. The “Identify Characteristics” functionor step 7-3 may characterize some or all of the auditory events by oneor more characteristics. Such characteristics may include anidentification of the dominant subband of the auditory event, asdescribed in connection with the process of FIG. 5. The characteristicsmay also include one or more of the MPEG-7 audio descriptors, including,for example, a measure of power of the auditory event, a measure ofamplitude of the auditory event, a measure of the spectral flatness ofthe auditory event, and whether the auditory event is substantiallysilent. The characteristics may also include other characteristics suchas whether the auditory event includes a transient. Characteristics forone or more auditory events are also received by the “Store and Format”function or step 7-3 and stored and formatted along with the auditoryevent information.

Alternatives to the arrangement of FIG. 7 are shown in FIGS. 8 and 9. InFIG. 8 block 8-1, 8-2 and 8-3 correspond, respectively to blocks 7-1,7-2 and 7-3 described above except that block 8-2 does not receive anaudio input as does block 7-2. Thus, in FIG. 8, the audio input signalis not applied directly to the “Identify Characteristics” function orstep 8-2, but block 8-2 does receive information from the “IdentifyAuditory Events” function or step 8-1. The arrangement of FIG. 5 is aspecific example of such an arrangement. In FIG. 9, the functions orsteps 9-1, 9-2 and 9-3 are arranged in series. Blocks 9-1, 9-2 and 9-3correspond, respectively, to blocks 7-1 (except that it provides only asingle output instead of two outputs), 8-2, and 7-3 (except that itreceives one input rather than two).

The details of this practical embodiment are not critical. Other ways tocalculate the spectral content of successive time segments of the audiosignal, calculate the differences between successive time segments, andset auditory event boundaries at the respective boundaries betweensuccessive time segments when the difference in the spectral profilecontent between such successive time segments exceeds a threshold may beemployed.

It should be understood that implementation of other variations andmodifications of the invention and its various aspects will be apparentto those skilled in the art, and that the invention is not limited bythese specific embodiments described. It is therefore contemplated tocover by the present invention any and all modifications, variations, orequivalents that fall within the true spirit and scope of the basicunderlying principles disclosed and claimed herein.

The present invention and its various aspects may be implemented assoftware functions performed in digital signal processors, programmedgeneral-purpose digital computers, and/or special purpose digitalcomputers. Interfaces between analog and digital signal streams may beperformed in appropriate hardware and/or as functions in software and/orfirmware.

The invention claimed is:
 1. A method for dividing an at least twochannel, digital audio signal into separate events, the methodcomprising: analyzing time concurrent samples of the audio signal acrossat least two channels to determine auditory event boundaries in channelsof the audio signal, and identifying a continuous succession of theauditory event boundaries in said channels of the audio signal, whereineach auditory event is between adjacent auditory event boundaries in achannel, each auditory event boundary representing an end of a precedingauditory event and a beginning of a next auditory event such that acontinuous succession of auditory events in said channels is obtained,identifying a combined auditory event boundary for said channels inresponse to the identification of an auditory event boundary in achannel, and wherein neither auditory event boundaries nor the auditoryevents are known in advance of identifying the continuous succession ofauditory event boundaries in said channels and obtaining the continuoussuccession of auditory events in said channels.
 2. The method of claim 1wherein at least one of the auditory event boundaries divides: (i) aportion of audio signal quiet relative to a transient and (ii) thetransient.
 3. The method of claim 1 wherein at least one of the auditoryevents is silent.
 4. The method of claim 1 wherein a block of the timeconcurrent samples represents less than 20 milliseconds of audio.
 5. Themethod of claim 1 wherein samples of the audio signal represent speech.6. The method of claim 1 wherein the analyzing provides contentinformation of the auditory events.
 7. The method of claim 1 wherein theat least two channels includes three channels.
 8. The method of claim 1wherein the at least two channels includes four channels.
 9. The methodof claim 5 wherein a component of speech is identified as an auditoryevent.